Add complete workflow for using TA-Lib to bootstrap training data:
- generate_talib_annotations.py: Python script to run TA-Lib CDL* functions
and output span annotations in UI-compatible format
- import_talib_annotations.ts: TypeScript script to import generated
annotations into the UI database with auto-label-type creation
- npm script 'import-annotations' for easy execution
- TALIB_WORKFLOW.md: Comprehensive guide covering the full cycle:
* Generate patterns with TA-Lib
* Import into UI
* Review and edit in browser
* Export and train model
* Compare predictions with TA-Lib detections
* Iterate for improvement
This enables the intended workflow: use TA-Lib for initial annotations,
manually refine them, then train a model that learns from corrections.
- Add fixed width to sidebar (w-64) to prevent layout collapse
- Change chart container from flex-1 to w-full h-full for proper sizing
- Chart now properly displays after CSV upload
- Created CandleChart component with lightweight-charts integration
- Implemented SvgOverlay component for line drawing
- Integrated all components in main page
- Fixed TypeScript and Tailwind CSS compatibility issues
- Added comprehensive README.md with project documentation
- Created DEPLOYMENT.md with setup and troubleshooting guide
- Downgraded to stable versions (Tailwind v3, lightweight-charts v4)
- All 59 tasks from OpenSpec completed
- Set up Next.js with App Router, TypeScript, Tailwind CSS
- Configure shadcn/ui with dark theme
- Install dependencies: lightweight-charts, papaparse, lucide-react
- Set up Drizzle ORM with better-sqlite3
- Create database schema for candles and annotations tables
- Generate migration SQL
